CN107526070A - The multipath fusion multiple target tracking algorithm of sky-wave OTH radar - Google Patents
The multipath fusion multiple target tracking algorithm of sky-wave OTH radar Download PDFInfo
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- CN107526070A CN107526070A CN201710970706.4A CN201710970706A CN107526070A CN 107526070 A CN107526070 A CN 107526070A CN 201710970706 A CN201710970706 A CN 201710970706A CN 107526070 A CN107526070 A CN 107526070A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
Abstract
The invention discloses a kind of sky-wave OTH radar multipath merge multiple target tracking algorithm, one：Initialize observing environment parameter；Two：Initialization batchparameters simultaneously makes batch processing iterations i=1；Three：Calculate each posteriority association probability measured with each target, per paths；Four：Calculate comprehensive measurement and comprehensive covariance of each target under every paths；Five：Parallel-expansion Kalman smoothing algorithm is used to all paths of each target, updates state parameter and state covariance；6th step：Judge whether to meet the loop iteration condition of convergence, be such as unsatisfactory for, make i=i+1 and return to the 3rd step；Otherwise in next step；Seven：There is confidence level in the target of renewal flight path, judge to export confirmation flight path if target has confidence level more than probability is confirmed；Exist if target if confidence level is less than probability of erasure and delete the flight path；Otherwise judge as interim flight path up for further；Eight：Batch processing sliding window forward slip TsAt the individual moment, return to second step.
Description
Technical field
The invention belongs to Radar Technology field, a kind of probability multiple hypotheis tracking of high efficient multi-path footpath information fusion is related generally to
(PMHT) algorithm, specifically a kind of signal multipath propagation feature for being based on sky-wave OTH radar (OTHR), to faint
The method that multiple target is tracked.This method is in distinguishable signal multipath propagation environment, by observing letter to multipath
Digital display type models, and to multipath observation information fusion treatment, so as to the faint multiple target under low Observable, high clutter conditions
It is tracked.
Background technology
Target following technology is widely used in each field, particularly radar signal system.Sky-wave OTH radar is one
Kind distant early warning rader, it realizes the detection of over the horizon scope using refraction of the frequency electromagnetic waves in ionosphere.Sky-wave beyond visual range
The operating distance of radar is not limited by earth curvature, and can all kinds of moving targets in monitored area be implemented with distant early warning, tool
There is typical four anti-performance (scouting-counterreconnaissance, interference-counter-measure, destruction-anti-destroy and stealthy-anti-stealthy).But compared to conventional
Radar, OTHR radars are mainly in face of difficulties such as low detection probability, low measurement accuracy, high false alarm rates.Simultaneously as multilayer ionosphere
Refracted electromagnetic ripple, forms a plurality of signal propagation path, and a target can produce multiple measurements.
Traditional multiple target tracking algorithm, such as multiple hypotheis tracking (MHT), all JPDA (JPDA), base
In a kind of " hard " decision-making measured with target association：One target at most produces a measurement.Therefore, not only easily produce false
Flight path, and target tracking accuracy is poor, loses with rate height.Based on these rudimentary algorithms, the target following for multipath feature is expanded
Exhibition algorithm allows multiple measurements and same target association to a certain extent, solves the problems, such as multipath false track, but these are calculated
Method needs exhaustive all possible correlating event, and its computation complexity is in the increase of number of targets, measurement number and number of path
Exponential increase, turn into NP problems.
Probability multiple hypotheis tracking algorithm (PMHT) is a kind of batch processing of complexity with measuring number and number of targets linear correlation
Target tracking algorism.This comes from " soft " decision-making which employs measurement and target association：One target can produce multiple measurements, and
Measure (can distribute to either objective) by all measurements independently of each other with associating for target.The core that PMHT algorithms are realized is in mesh
Mark with measure associate it is unknown in the case of, based on it is expected maximum (EM) algorithm obtain dbjective state maximum a posteriori (MAP) estimate.
Before, PMHT algorithms were once applied to OTHR scenes, but it does not distinguish the characteristic in different paths, only with a kind of measurement model
Measurement from different paths is dealt with, its result causes the false track of target to produce, and target tracking accuracy is poor, loses
With rate height.
The content of the invention
It is an object of the invention to provide a kind of multipath of sky-wave OTH radar to merge multiple target tracking algorithm, extension
The deficiency that traditional PMHT algorithms are applied in multi-path environment.
The purpose of the present invention is achieved through the following technical solutions：
A kind of multipath fusion multiple target tracking algorithm of sky-wave OTH radar, is comprised the steps of：
The first step：Initialize the observing environment parameter of PMHT algorithms；Observing environment parameter includes：Slant range variance, side
Position variance, Doppler variance, false-alarm probability, detection probability, clutter density λ, sampling interval, the volume V of monitoring space, number of path
Mesh L.
Second step：Initialize the batchparameters of PMHT algorithms and make batch processing iterations i=1；Batchparameters bag
The X of parameter containing dbjective state, dbjective state covariance P, metric data Z, target are with measuring the prior probability Π associated.
Parametric variable under target following scene can be expressed as：
X=(X (1) ..., X (t) ..., X (TB))
Z=(Z (1) ..., Z (t) ..., Z ((TB))
K=(K (1) ..., K (t) ..., K (TB))
Wherein TBThe sampling instant number of batch processing is represented, X represents TBThe dbjective state parameter at individual moment, P represent corresponding mesh
State covariance is marked, Z represents TBThe measurement set at individual moment, K represent one group of unknown and unobservable random variable,
X (t)=(x1(t),...,xn(t),...,xN(t)) n=1 ..., N
Wherein, xn(t) state of n-th of target of t, P are representedn(t) corresponding state covariance is represented, N represents target
Number, zj(t) j-th of metric data of t, m are representedtRepresent the metric data number of t, kj,l(t) t jth is represented
The target in individual measurement passage path l sources, its prior probability form are represented by：
p(kj,l(t)=n)=πn,l(t)
Here πn,l(t) represent that measuring passage path l derives from target xn(t) prior probability, then target with measure associate
Prior probability Π be expressed as：
Π=(Π (1) ..., Π (t) ..., Π (TB))
Π (t)=(π1,1(t),...,πn,l(t),...,πN,L(t)) n=1 ..., Nl=1 ..., L
Moving equation and the observational equation explicitly modeled to different paths, are expressed as：
xn(t)=Fn(t)xn(t-1)+νn(t) n=1 ..., N
zn,l(t)=hn,l(xn(t))+ωn,l(t) l=1 ..., L
Wherein Fn(t) it is kinematic matrix, hn,l() is target xn(t) observation function through path l.νn(t)、ωn,l(t)
Respectively process noise and observation noise, and hypothesis is the white Gaussian noise of zero-mean, and covariance matrix is respectively Qn(t)
And Rn,l(t)。zn,l(t) target x is representedn(t) target caused by passage path l measures.
3rd step：Calculate each posteriority association probability measured with each target, per paths：
Wherein：N=1 ..., N, j=1 ...,
mt, l=1 ..., L, in formula, Rn,l(t) error covariance matrix of observation noise is represented,Represent t n-th of target the
The Target state estimator of i interative computation, zj(t) j-th of measurement data that t obtains, k are representedj,l(t) t the is represented
The j target for measuring passage path l sources；N{χ；μ, Σ } Gaussian probability-density function is represented, gaussian variable χ average is μ,
Covariance is Σ, andπn,l(t) it is general from target n priori to measure passage path l
Rate, π0(t) clutter prior probability is represented, V represents the volume in monitoring space；
4th step：Calculate comprehensive measurement and comprehensive measurement covariance of each target under every paths；
5th step：Parallel-expansion Kalman smoothing algorithm is used to all paths of each target, obtains TBThe individual moment
State parameter and state covariance；
(1) to target xn(t) measurement matrix under different paths, comprehensive measurement, the comprehensive covariance that measures carry out heap
It is folded；To measuring function hn,l() asks gradient to obtain measurement matrix Hn,l(t), must stack measurement matrix is：
Stack comprehensive measurement respectively again and the comprehensive covariance that measures is：
Diag () represents diagonalizable matrix in formula.
(2) to target xn(t) parallel-expansion Kalman smoothing algorithm is performed.First, carry out smooth forward
xn(t | t-1)=Fn(t)xn(t-1|t-1)
Pn(t | t-1)=Fn(t)Pn(t-1|t-1)Fn(t)′+Qn(t)
Afterwards, carry out smooth backward：
Cn(t)=Pn(t|t)Fn(t)′Pn(t+1|t)-1
xn(t | N)=xn(t|t)+Cn(t)[xn(t+1|N)-xn(t+1|t)]
Pn(t | N)=Pn(t|t)+Cn(t)[Pn(t+1|N)-Pn(t+1|t)]Cn(t)′
In formula, Pn(t | t) and Pn(t | t-1) it is respectively target xn(t) error covariance estimation and error prediction covariance
Estimation, xn(t | t) and xn(t | t-1) it is respectively that the state estimation of target and status predication are estimated, xn(t | N) and Pn(t | N) be to
Smooth Target state estimator is estimated with error covariance afterwards, Qn(t) it is the error covariance matrix of radar surveying, Wn(t) it is increasing
Benefit, Fn(t) it is kinematic matrix.
6th step：Judge whether to meet the loop iteration condition of convergence, be such as unsatisfactory for, make i=i+1 and return to the 3rd step；It is no
Then perform next step；
7th step：Be present confidence level in the target of renewal flight path, judge defeated if target has confidence level more than probability is confirmed
Go out to confirm flight path；Exist if target if confidence level is less than probability of erasure and delete the flight path；Otherwise as interim flight path up for entering
The judgement of one step；
8th step：Batch processing sliding window forward slip TsAt the individual moment, return and perform second step.
The beneficial effects of the present invention are：The present invention carries out explicitly modeling and information fusion to OTHR multipaths observation process
Processing, while measurement and the computation complexity of target association are effectively reduced using PMHT algorithmic characteristic, can be effectively using a plurality of
The measurement information in path, target is significantly reduced while Target state estimator precision is improved and is lost with rate.
Brief description of the drawings
Fig. 1 is the position and measurement model geometric graph of target and sensor under over-the-horizon radar.
Fig. 2 is that signal is from emitter sensor to target again to the propagation of receiver sensor under two kinds of ionospheres E and F
Path profile.Respectively this 4 kinds of propagation paths of EE, EF, FE and FF, corresponding 4 kinds of measurement models.
Fig. 3 is the slant range of lower 40 sampling instants of 2 target environments.In figure：Clutter is represented with black round dot, by EE
Measurement is represented with circle caused by propagation path, and the measurement as caused by EF propagation paths is represented with square, by FE propagation paths
Caused measurement is represented with rhombus, is measured as caused by FF propagation paths and is represented with star.Same propagation path is passed through by target 1
Caused measurement is connected with solid line, and the measurement as caused by target 2 by same propagation path is connected with dotted line.
Fig. 4 is the Doppler of lower 40 sampling instants of 2 target environments.
Fig. 5 is the angle of inclination observation of lower 40 sampling instants of 2 target environments.
Fig. 6 is multipath PMHT algorithm multiple target tracking result figures under 2 target environments.
Fig. 7 is that the multipath of sky-wave OTH radar merges the schematic flow sheet of multiple target tracking algorithm.
3rd, specific implementation
In order that the object, technical solutions and advantages of the present invention are clearer, below by the specific embodiment party of the present invention
Formula is described in further detail.
As shown in fig. 7, the multipath fusion multiple target tracking algorithm of sky-wave OTH radar comprises the steps of：
The first step：Initialize the observing environment parameter of PMHT algorithms.
In sky-wave OTH radar application scenarios, it is anti-by ionosphere that receiver sensor is fixed on [0km, 0km] collection
The signal come is emitted back towards, emitter sensor is fixed on [100km, 0km].It suppose there is two preferable ionosphere E and F such as Fig. 1 institutes
Show, they there should be two fixed height h relativelyE=100km and hF=220km, then signal is from emitter sensor to target
Arriving receiver sensor again has EE, EF, FE and FF totally 4 kinds of propagation paths, and sets the detection probability of every paths observationIt is
0.4。
The oblique distance scope that monitor area is corresponding to measure space be 1 100~1 350km, Doppler spread for 0.135~
0.155km/s, azimuth coverage are 0.09~0.17rad, and metric data is expressed as zj(t)=[Rgj(t),Rrj(t),Azj
(t)]T, wherein Rg (t), Rr (t), Az (t) is respectively oblique distance, Doppler and direction angle measurements.Measuring standard difference is respectively：σRg
=5km, σRr=0.001km/s, σAz=0.003rad.It is assumed that clutter is uniformly distributed in unit, and average each moment is miscellaneous
Wave number Nλ=10.
Interval delta T between radar system sampling instant is 20s, and emulation total duration is 40 moment, each batch processing
Duration TBFor 3 moment, sliding length TsFor 2 moment.Fixed cycles iterations is used as 5 times in per batch processing.Sampling
During, have 2 targets be divided into using the motion vector of original state as：
x1=[1100km 0.15km/s 0.10472rad 8.72665e-5rad/s]
x2=[1130km 0.15km/s 0.10472rad 8.72665e-5rad/s]
Do linear uniform motion；
Second step：Initialize the batchparameters of PMHT algorithms and make batch processing iterations i=1.
N number of object initialization state and corresponding dbjective state are obtained by target detection initialization algorithm or target are default
Covariance：
X (1)=(x1(1),...,xn(1),...,xN(1)) n=1 ..., N
P (1)=(P1(1),...,Pn(1),...,PN(1)) n=1 ..., N
The metric data Z that sensor receives is as shown in Figure 3-Figure 5.From state of ground parameter coordinate
Mapping to sensor observation coordinate [Rg Rr Az] is that observation model can be obtained by Fig. 2 geometrical model：
η=ρ-dsin (b)
Rg=r1+r2
Az=sin-1{ρsin(b)/(2r1)}
Wherein hrAnd htIonosphere E and F height, as 4 kinds of different observation models are substituted for respectively.
Target is calculated as follows with measuring the prior probability Π associated：
3rd step：Calculate each posteriority association probability measured with each target, per paths
4th step：Calculate comprehensive measurement of each target under every pathsAnd comprehensive covariance
5th step：Parallel-expansion Kalman smoothing algorithm is used to all paths of each target, obtains TBThe individual moment
State parameter and state covariance.
(1) to target xn(t) measurement matrix under different paths, comprehensive measurement, the comprehensive covariance that measures are stacked
Obtain stacking measurement matrixStack comprehensive measureStack comprehensive measurement covariance
(2) to target xn(t) x after parallel-expansion Kalman smoothing algorithm is updated is performednAnd P (t)n(t)；
6th step：Judge whether to meet the loop iteration condition of convergence, for example whether i=5, such as no, make i=i+1 and return
3rd step；Otherwise next step is performed.
7th step：There is confidence level in the target of renewal flight path, judge to export if target has probability more than probability is confirmed
Confirm flight path；Exist if target if probability is less than probability of erasure and delete the flight path；Otherwise as interim flight path up for further
Judgement.
8th step：Batch processing sliding window forward slip TsThe individual moment, that is, receive new TsIndividual moment metric data, before giving up
TsIndividual moment metric data.Return and perform second step.
This example implementation in, in Fig. 6 blue solid lines be under low detection probability, the OTHR environment of high clutter to multiple target with
The track result that track is 100 times, and mistake of the statistical result to two targets is 0 with number.Its result shows, track
Very close with the true flight path of target, the PMHT algorithms of multipath maintain stable tracking to multiple target, both improve target
Precision of state estimation, target is significantly reduced again and is lost with rate.
Finally illustrate, the above is implemented to be merely illustrative of the technical solution of the present invention and unrestricted, all according to Shen of the present invention
Please the equivalent change done of the scope of the claims and modification, should all belong to the covering scope of the present invention.
Claims (4)
1. a kind of multipath fusion multiple target tracking algorithm of sky-wave OTH radar, is comprised the steps of：
The first step：Initialize the observing environment parameter of PMHT algorithms；
Second step：Initialize the batchparameters of PMHT algorithms and make batch processing iterations i=1；
3rd step：Calculate each posteriority association probability measured with each target, per paths：
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In formula, L represents the path number that radar is propagated, and N represents number of targets, mtRepresent the metric data number of t, Rn,l(t)
Represent the error covariance matrix of observation noise；Represent that the dbjective state of n-th of target ith iteration computing of t is estimated
Meter, zj(t) j-th of measurement data that t obtains, k are representedj,l(t) j-th of measurement passage path l source of t is represented
Target；N{χ；μ, Σ } represent Gaussian probability-density function, gaussian variable χ average is μ, covariance Σ, andπn,l(t) target n prior probability, π are derived from for measurement passage path l0(t) represent miscellaneous
Ripple prior probability, V represent the volume in monitoring space；
4th step：Calculate comprehensive measurement and comprehensive measurement covariance of each target under every paths；
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5th step：Parallel-expansion Kalman smoothing algorithm is used to all paths of each target, obtains TBThe state ginseng at individual moment
Number and state covariance；
6th step：Judge whether to meet the loop iteration condition of convergence, be such as unsatisfactory for, make i=i+1 and return to the 3rd step；Otherwise hold
Row is in next step；
7th step：There is confidence level in the target of renewal flight path, judge to export really if target has confidence level more than probability is confirmed
Recognize flight path；Exist if target if confidence level is less than probability of erasure and delete the flight path；Otherwise as interim flight path up for further
Judgement；
8th step：Batch processing sliding window forward slip TsAt the individual moment, return and perform second step.
2. a kind of multipath fusion multiple target tracking algorithm of sky-wave OTH radar according to claim 1, its feature
It is that the observing environment parameter includes：Slant range variance, orientation variance, Doppler variance, false-alarm probability, detection probability,
Clutter density λ, sampling interval, the volume V of monitoring space, path number L.
3. a kind of multipath fusion multiple target tracking algorithm of sky-wave OTH radar according to claim 1, its feature
It is that the batchparameters includes dbjective state parameter, dbjective state covariance, metric data, target are with measuring the elder generation associated
Test probability.
4. a kind of multipath fusion multiple target tracking algorithm of sky-wave OTH radar according to claim 1, its feature
It is that the 5th step comprises the steps of：
(1) to target xn(t) measurement matrix under different paths, comprehensive measurement, the comprehensive covariance that measures are stacked to obtain
Stack measurement matrixStack comprehensive measureStack comprehensive measurement covariance
(2) to target xn(t) progress is smooth forward, obtains：
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Wherein,
Pn(t | t-1)=Fn(t)Pn(t-1|t-1)Fn(t)′+Qn(t)
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In formula, Pn(t | t) and Pn(t | t-1) it is respectively target xn(t) error covariance estimation is estimated with error prediction covariance
Meter, xn(t | t) and xn(t | t-1) it is respectively that the state estimation of target and status predication are estimated, Qn(t) it is the error of radar surveying
Covariance matrix, Wn(t) it is gain, Fn(t) it is kinematic matrix；
(3) to target xn(t) progress is smooth backward, obtains：
xn(t | N)=xn(t|t)+Cn(t)[xn(t+1|N)-xn(t+1|t)]
Pn(t | N)=Pn(t|t)+Cn(t)[Pn(t+1|N)-Pn(t+1|t)]Cn(t)′
Wherein, Cn(t)=Pn(t|t)Fn(t)′Pn(t+1|t)-1, xn(t | N) and Pn(t | N) estimate for dbjective state smooth backward
Meter is estimated with error covariance.
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| CN108919225A (en) * | 2018-07-26 | 2018-11-30 | 中国人民解放军海军航空大学 | Range extension target Multichannel fusion detection method under the uniform environment in part |
| CN108919253A (en) * | 2018-05-17 | 2018-11-30 | 哈尔滨工业大学 | A kind of folded Clutter in Skywave Radars multi-path data correlating method based on empire's Competitive Algorithms |
| CN109509207A (en) * | 2018-11-09 | 2019-03-22 | 电子科技大学 | The method that a kind of pair of point target and extension target carry out seamless tracking |
| CN110376581A (en) * | 2019-06-24 | 2019-10-25 | 河海大学 | The explicit multi-object tracking method of density filter is assumed based on Gaussian-mixture probability |
| CN110780269A (en) * | 2019-10-08 | 2020-02-11 | 河海大学 | Explicit multi-target tracking method based on GM-PHD filter under self-adaptive new growth intensity |
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| CN108241147A (en) * | 2018-02-06 | 2018-07-03 | 上海圆舟电子科技有限公司 | A kind of palm intelligent maritime affairs radar and its surface surveillance method |
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| CN108919253A (en) * | 2018-05-17 | 2018-11-30 | 哈尔滨工业大学 | A kind of folded Clutter in Skywave Radars multi-path data correlating method based on empire's Competitive Algorithms |
| CN108919253B (en) * | 2018-05-17 | 2022-06-21 | 哈尔滨工业大学 | Sky wave radar multi-path data association method based on empire competition algorithm |
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| CN109509207B (en) * | 2018-11-09 | 2021-07-06 | 电子科技大学 | Method for seamless tracking of point target and extended target |
| CN110376581A (en) * | 2019-06-24 | 2019-10-25 | 河海大学 | The explicit multi-object tracking method of density filter is assumed based on Gaussian-mixture probability |
| CN110376581B (en) * | 2019-06-24 | 2022-03-25 | 河海大学 | Explicit multi-target tracking method based on Gaussian mixture probability hypothesis density filter |
| CN110780269A (en) * | 2019-10-08 | 2020-02-11 | 河海大学 | Explicit multi-target tracking method based on GM-PHD filter under self-adaptive new growth intensity |
| CN110780269B (en) * | 2019-10-08 | 2022-03-25 | 河海大学 | Explicit multi-target tracking method based on GM-PHD filter under self-adaptive new growth intensity |
| CN111767639A (en) * | 2020-05-25 | 2020-10-13 | 西北工业大学 | Multi-sensor track association method |
| CN111708015B (en) * | 2020-07-13 | 2022-06-21 | 哈尔滨工业大学 | Low-altitude target tracking filtering method under multipath effect |
| CN111708015A (en) * | 2020-07-13 | 2020-09-25 | 哈尔滨工业大学 | Low-altitude target tracking filtering method under multipath effect |
| CN112731373A (en) * | 2020-12-24 | 2021-04-30 | 西安理工大学 | External radiation source radar multi-target tracking method based on three-dimensional data association |
| CN112731373B (en) * | 2020-12-24 | 2023-09-22 | 西安理工大学 | External radiation source radar multi-target tracking method based on three-dimensional data association |
| CN113625232A (en) * | 2021-07-07 | 2021-11-09 | 嘉兴聚速电子技术有限公司 | Method, device, medium and equipment for suppressing multipath false target in radar detection |
| CN113625232B (en) * | 2021-07-07 | 2023-11-07 | 嘉兴聚速电子技术有限公司 | Method, device, medium and equipment for restraining multipath false target in radar detection |
| CN114942428A (en) * | 2022-05-27 | 2022-08-26 | 哈尔滨工业大学 | Multi-target tracking mixed system radar data association algorithm |
| CN114942428B (en) * | 2022-05-27 | 2024-04-09 | 哈尔滨工业大学 | Multi-target tracking hybrid radar data association algorithm |
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